• Design, develop and maintain corporate data platforms that enable insights from customer data • Apply expertise in SQL and Python to execute projects and tasks with high quality, high accuracy and within established deadlines • Design, code, test, debug and document programs and scripts using agreed standards and tools, ensuring well-structured deliverables • Ensure data quality and implement tools and frameworks to automate the detection of data quality issues • Profile data sources and develop ETL processes, with a solid understanding of data modeling fundamentals, using SQL, Python and ETL/ELT support tools • Assist management in preparing estimates and proposals for clients • Plan effective solutions for data storage, security, sharing and publishing across the organization • Create and update documentation of process flows and business rules • Maintain existing pipelines and process large volumes of data as required

• To design, development, and enhancement of our AWS data environment.

• Participate in cross-functional meetings to define project objectives and scope. • Analyze and prioritize business requirements related to data. • Design and develop data extraction, transformation, and loading pipelines (ETL/ELT). • Develop applications for extracting data from various sources and formats (JSON, XML, PDF, etc.). • Write clean, efficient, and scalable code, adapting to new technologies as needed. • Implement data quality methodologies throughout ingestion and transformation processes. • Optimize services, queries, and processes to reduce operational costs and improve performance. • Validate the functionality, integrity, and security of data engineering processes.

• Design, develop, and maintain scalable and efficient data pipelines (ETL/ELT). • Build and optimize modern data architectures (Data Warehouse and Lakehouse). • Implement data models (primarily dimensional modeling using the Kimball approach). • Integrate multiple data sources ensuring quality, consistency, and availability. • Perform data profiling and anomaly detection. • Optimize SQL queries and performance across data platforms. • Collaborate on defining standards, best practices, and data architecture. • Implement CI/CD processes and DevOps practices applied to data pipelines. • Work under Agile methodologies (Scrum/Kanban) in collaboration with multidisciplinary teams.